Prerequisites & What is Pytorch? ● Training & Testing Neural Networks in Pytorch ● Dataset & Dataloader ● Tensors ● torch.nn: Models, Loss Functions ● torch.optim: Optimization ● Save/load models Prerequisites __getitem__(4) 0 1 2 3 4 Tensors ● High-dimensional matrices (arrays) 1-D tensor e.g. audio 2-D tensor e.g. black&white images 3-D tensor e.g. RGB images Tensors – Shape of Tensors ● Check with .shape() dim in PyTorch == axis in NumPy dim 0 dim 0 dim 1 dim 0 dim 1 dim 2 5 5 3 5 4 3 Tensors – Creating Tensors ● Directly from data (list or numpy.ndarray) x = torch.tensor([[1, -1], [-1, 1]]) x

the basic neural network operation is as follows: f(X; W, b) = σ(XW + b) Here, X, W and b are tensors (mathematical term for an n-dimensional matrix) to denote inputs, weights and the bias respectively etc. A neural network model learns W and b tensors which are stored with the model. Hence, they contribute significantly to its size. Of the two tensors, W naturally dominates the size owing to its higher weights to reduce a model’s size: 3The terms tensor and matrix are used interchangeably in the text. Tensors are N-dimensional matrices. The shape of a tensor denotes the size of each of its dimensions, and

shape. >>> k.int_shape(data) (1, 3, 3) dot It is used to multiply two tensors. Consider a and b are two tensors and c will be the outcome of multiply of ab. Assume a shape is (4,2) and b shape the input tensors of the model as list. >>> inputs = model.inputs >>> inputs []  model.outputs: Returns all the output tensors of the model is_categorical_crossentropy All above loss function accepts two arguments:  y_true - true labels as tensors  y_pred - prediction with same shape as y_true Import the losses module before using loss function

number of LSTM cells based on the sentence’s length. PyTorch • Fundamental Concepts of PyTorch • Tensors • Autograd • Modular structure • Models / Layers • Datasets • Dataloader • Visualization Tools like examples Train Model •Train weights Evaluate Model •Visualise Tensor • Tensor? • PyTorch Tensors are just like numpy arrays, but they can run on GPU. • Examples: And more operations like: Indexing of operations for autograd • t.grad_fn Loading Data, Devices and CUDA • Numpy arrays to PyTorch tensors • torch.from_numpy(x_train) • Returns a cpu tensor! • PyTorch tensor to numpy • t.numpy() • Using

compile(self, optimizer, loss, metrics=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None) 用于配置训练模型。 参数 • optimizer: 字符串（优化器名）或者优化器对象。详见 optimizers。 • loss: 字符串（目标函数名）或目标函数。详见 l class_weight 评估和加权的度 量标准列表。 • target_tensors: 默认情况下，Keras 将为模型的目标创建一个占位符，在训练过程中将使用 目标数据。相反，如果你想使用自己的目标张量（反过来说，Keras 在训练期间不会载入 这些目标张量的外部 Numpy 数据），您可以通过 target_tensors 参数指定它们。它应该 是单个张量（对于单输出 Sequential loss, metrics=None, loss_weights=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=None) 用于配置训练模型。 参数 • optimizer: 字符串（优化器名）或者优化器对象。详见 optimizers。 • loss: 字符串（目标函数名）或目标函数。详见 l

classification algorithm 1. Load model and labels 2. Build interpreter 3. Allocate input and output tensors 4. Read image 5. Resize image 6. Copy resized image to input tensor 7. Run inference 8. Extract std::unique_ptr interpreter; 11 builder(&interpreter); 20 / 503. Allocate input and output tensors 1 // defined and properly initialized elsewhere: 2 // std::unique_ptr interpreter;

wavelength, position vector, ...) • Quantities may have different character – scalars – vectors – tensors CppCon 2024: Improving our safety with a quantities and units library Introducing quantity_spec wavelength, position vector, ...) • Quantities may have different character – scalars – vectors – tensors • Quantities may be defined as non-negative CppCon 2024: Improving our safety with a quantities

instructions on multiple data - OpenMP - Use-cases - Vectors, matrices, multi-dimensional arrays and tensors - Challenges: - Nested parallelism - Splitting the loop - Static splitting - Eager binary splitting

introduced. . . Stateless expression templates Old-school Expression Templates Data (vectors, matrices, tensors. . . ) serve as terminal symbols Expression templates are stateful Stateless Expression Templates

Miscellaneous P1416: Linear Algebra for Data Science and Machine Learning P1770: On vectors, tensors, matrices, and hypermatrices CppCon - Vincent Reverdy - September 17th, 2020 - Online CC0 1.0 Universal